Senior Data Engineer - PubTech Data, Insights & Analytics, Amazon

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Industry experience in advertising
- 2+ years of experience building applications using the AWS cloud or similar

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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $139,100/year in our lowest geographic market up to $240,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
The Publisher Technologies (PubTech) team builds and operates extensible services that empower 1P Publishers to improve the monetization of their customer experiences, along with the experiences themselves. We bias toward standards-based and flexible designs that allow Publishers the ability to invent on top of our solutions and to interoperate well with other advertising technology providers; both internal and external.

The PubTech Data, Insights, and Analytics team enables faster data-driven decision making for Publishers and Monetization teams by providing them with near real time data, data management tools, actionable insights, and an easy-to-use reporting experience. Our data products provide Publishers and Monetization teams with the capabilities necessary to better understand the performance of their Advertising products along with supporting machine learning at scale.

As a Senior Data Engineer, you will be responsible for building a reliable and efficient data platform to support Analytics and intuitive data products. This will enable our stakeholders to easily leverage data in an efficient and self-service manner. You will work on diverse, industry-leading data technologies such as Spark, Iceberg, Kafka, and various other AWS technologies to build a scalable and robust data platform supporting Analytics and Experimentation. Additionally, you will own the creation of end-to-end data pipelines that consolidate large data sets across disparate systems and combine them into easily consumable data sets, driving actionable insights for your customers.

The ideal candidate will be highly analytical, able to work effectively in a matrix organization, and adept at synthesizing a variety of technologies and capabilities into products that advertisers love. You must engage with customers to deeply understand their current and emerging needs. Given the rapidly evolving nature of advertising, you must develop customer insight to identify solutions and innovate on behalf of your customers. We are seeking a customer-obsessed, technology-savvy individual with a passion for advertising and a well-rounded technical background, including a history of leading complex, ambiguous projects end-to-end.

Key job responsibilities
- Design, develop, and maintain our PubTech data platform's processing and analytics needs.
- Build robust, scalable, fault-tolerant data ingestion, processing, and storage solutions from the ground up using technologies such as Apache Spark, Kafka, and Amazon Web Services (AWS).
- Implement data quality processes, monitoring, and alerting mechanisms.
- Collaborate with data scientists, analysts, and product teams to understand their data requirements and translate them into efficient and reliable data pipelines.
- Contribute to the team's efforts in developing best practices, standards, and tools for data engineering.
- Mentor and guide junior data engineers, fostering their professional growth and development.